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Abstract:

Search results may be yielded based on a search query. A search query
inspired by a user may be received. Based on the search query, multiple
search results each including links may be determined. The multiple
search results may include a first search result that includes a link to
a first digital instance that describes or embodies a first content item,
and a second search result that includes a link to a second digital
instance that describes or embodies a second content item. A perceived
popularity may be assessed for each of the first and second content
items. The perceived popularity may be a measure of the popularity of the
first and second content items, and may be distinct from a popularity of
the first and second digital instances. A presentation of the multiple
search results may be determined based on the accessed perceived
popularity.

Claims:

1-17. (canceled)

18. A computer-implemented method for providing search results, the
method comprising: determining, based on a received search query, a first
search result and a second search result; identifying a first genre
related to the first search result and a second genre related to the
second search result; accessing a first genre-specific catalog associated
with the first genre and a second genre-specific catalog associated with
the second genre; determining, using at least one processor, a first
content item included in the first genre-specific catalog that is
associated with the first search result and a second content item
included in the second genre-specific catalog that is associated with the
second search result; and determining, using the at least one processor,
a presentation of the first search result and the second search result
based on a perceived popularity of the first content item and a perceived
popularity of the second content item.

19. The method of claim 18, wherein the first genre-specific catalog
includes information specifying the perceived popularity of the first
content item and the second genre-specific catalog includes information
specifying the perceived popularity of the second content item.

20. The method of claim 18, wherein: the first search result includes a
link to a first digital instance that specifies the first content item;
and the second search result includes a link to a second digital instance
that specifies the second content item.

21. The method of claim 18, wherein the first content item is one of a
musical artist, a song, or a music album.

22. The method of claim 18, wherein the perceived popularity of the first
content item and the perceived popularity of the second content item is
determined by: forming a popularity query for the first content item and
a popularity query for the second content item; and determining search
results for the popularity queries for the first and second content items
by searching for digital instances that include information that
satisfies the popularity queries.

23. The method of claim 22, wherein: the perceived popularity of the
first content item is based on a number of search results determined for
the popularity query for the first content item; and the perceived
popularity of the second content item is based on a number of search
results determined for the popularity query for the second content item.

24. The method of claim 22, wherein the popularity query for the first
content item and the popularity query for the second content item are
formed after receiving the search query.

25. A non-transitory computer-readable storage medium storing
instructions that are executable by at least one processor to cause the
at least one processor to execute a method, the method comprising:
determining, based on a received search query, a first search result and
a second search result; identifying a first genre related to the first
search result and a second genre related to the second search result;
accessing a first genre-specific catalog associated with the first genre
and a second genre-specific catalog associated with the second genre;
determining a first content item included in the first genre-specific
catalog that is associated with the first search result and a second
content item included in the second genre-specific catalog that is
associated with the second search result; and determining a presentation
of the first search result and the second search result based on a
perceived popularity of the first content item and a perceived popularity
of the second content item.

26. The non-transitory computer-readable storage medium of claim 25,
wherein the first genre-specific catalog includes information specifying
the perceived popularity of the first content item and the second
genre-specific catalog includes information specifying the perceived
popularity of the second content item.

27. The non-transitory computer-readable storage medium of claim 25,
wherein: the first search result includes a link to a first digital
instance that specifies the first content item; and the second search
result includes a link to a second digital instance that specifies the
second content item.

28. The non-transitory computer-readable storage medium of claim 25,
wherein the first content item is one of a musical artist, a song, or a
music album.

29. The non-transitory computer-readable storage medium of claim 25,
wherein the perceived popularity of the first content item and the
perceived popularity of the second content item is determined by: forming
a popularity query for the first content item and a popularity query for
the second content item; and determining search results for the
popularity queries for the first and second content items by searching
for digital instances that include information that satisfy the
popularity queries.

30. The non-transitory computer-readable storage medium of claim 29,
wherein: the perceived popularity of the first content item is based on a
number of search results determined for the popularity query for the
first content item; and the perceived popularity of the second content
item is based on a number of search results determined for the popularity
query for the second content item.

31. The non-transitory computer-readable storage medium of claim 29,
wherein the popularity query for the first content item and the
popularity query for the second content item are formed after receiving
the search query.

32. An electronic apparatus, comprising: at least one processor; and a
memory device that stores instructions, wherein the at least one
processor executes the instructions to: determine, based on a received
search query, a first search result and a second search result; identify
a first genre related to the first search result and a second genre
related to the second search result; access a first genre-specific
catalog associated with the first genre and a second genre-specific
catalog associated with the second genre; determine a first content item
included in the first genre-specific catalog that is associated with the
first search result and a second content item included in the second
genre-specific catalog that is associated with the second search result;
and determine a presentation of the first search result and the second
search result based on a perceived popularity of the first content item
and a perceived popularity of the second content item.

33. The electronic apparatus of claim 32, wherein the first
genre-specific catalog includes information specifying the perceived
popularity of the first content item and the second genre-specific
catalog includes information specifying the perceived popularity of the
second content item.

34. The electronic apparatus of claim 32, wherein: the first search
result includes a link to a first digital instance that specifies the
first content item; and the second search result includes a link to a
second digital instance that specifies the second content item.

35. The electronic apparatus of claim 32, wherein the perceived
popularity of the first content item and the perceived popularity of the
second content item is determined by: forming a popularity query for the
first content item and a popularity query for the second content item;
and determining search results for the popularity queries for the first
and second content items by searching for digital instances that include
information that satisfy the popularity queries.

36. The electronic apparatus of claim 35, wherein: the perceived
popularity of the first content item is based on a number of search
results determined for the popularity query for the first content item;
and the perceived popularity of the second content item is based on a
number of search results determined for the popularity query for the
second content item.

37. The electronic apparatus of claim 35, wherein the popularity query
for the first content item and the popularity query for the second
content item are formed after receiving the search query.

[0002] This description relates to popularity of electronic content items.

BACKGROUND

[0003] Users seeking information can search for the information on the
Internet. To do so, the user enters a search query into a search engine.
In response, the user receives search results that are relevant to the
search query. If the user seeks search results in a particular content
type or format, such as, for example, audio or video search results, a
user can include one of the words "audio" or "video" in the search query
along with words that relate to the content the user seeks.

SUMMARY

[0004] In a general aspect, search results are yielded based on a search
query. A search query inspired by a user is received. Based on the search
query, multiple search results, each including links, are determined. A
first search result includes a link to a first digital instance that
describes or embodies a first content item and a second search result
includes a link to a second digital instance that describes or embodies a
second content item. A perceived popularity is assessed for each of the
first and second content items. The perceived popularity is a measure of
the popularity of the first and second content items and is distinct from
a popularity of the first and second digital instances that describes or
embodies each of the first and second content items. A presentation of
the multiple search results is determined based on the accessed perceived
popularity.

[0005] With respect to at least the general aspect, implementations may
include one or more of the following features. For example, the multiple
search results may be displayed to the user based on the determined
presentation.

[0006] A genre related to the search query inspired by the user may be
received. A genre-specific catalog that is associated with the genre may
be identified. The genre-specific catalog may be accessed. One or more
content items stored within the genre-specific catalog that are related
to the determined multiple search results may be identified. Assessing
the perceived popularity for the search results may include accessing a
perceived popularity for each of the identified one or more content items
from within the genre-specific catalog.

[0007] An indication of a genre may be received. Multiple search results
that are associated with the genre may be identified. A genre-specific
catalog specific to the genre may be accessed. One or more content items
may be identified from within the genre-specific catalog that relate to
the identified multiple search results. A perceived popularity for the
identified one or more content items may be assessed. The presentation of
the multiple search results may be determined based on the assessed
perceived popularity.

[0008] One content item may be selected from among the one or more content
items stored within the genre-specific catalog. A perceived popularity
for the content item may be formed based on information associated with
the content item. Popularity search results may be determined by
searching private and public networks for digital instances that include
information that satisfies the popularity query. A perceived popularity
may be generated for the content item based on the popularity search
results. The generated perceived popularity may be associated with the
content item. The association may be stored in the genre-specific
catalog.

[0009] Generating a perceived popularity for the content item may include
determining a raw popularity score based at least in part on a number of
popularity search results received, using a classifier to analyze at
least some of the received popularity search results, determining a
number of popularity search results related to the genre of the catalog
based on the analysis performed by the classifier, determining an
ambiguity ratio that may include the determined number of popularity
search results that are related to the genre of the catalog and the
number of received popularity search results analyzed by the classifier,
and generating a perceived popularity for the content item based on the
raw popularity score and the ambiguity ratio.

[0010] The content item may be a song. The content item may be a musical
album, which may be an electronic organization of songs.

[0011] Generating a perceived popularity for the musical album may include
identifying songs related to the musical album, determining a perceived
popularity for each of the songs, determining a preliminary perceived
popularity for the musical album as a whole, and determining the
perceived popularity for the musical album based on the perceived
popularity for each of the songs and the preliminary perceived popularity
for the musical album as a whole. The preliminary perceived popularity
for the musical album as a whole may be based on popularity of the
musical album without regard to popularity of individual songs included
on the musical album.

[0012] The content item may be a musical artist, which may include one or
more people who write or perform music.

[0013] Generating a perceived popularity for the musical artist may
include identifying songs related to the musical artist, identifying
musical albums related to the musical artist, determining if the musical
artist may be popular for a non-music related reason, and determining
individual attributes related to the non-music related reason,
determining a perceived popularity for each of the songs, determining a
perceived popularity for each of the musical albums, determining a
perceived popularity for the musical artist based on the individual
attributes related to the non-music related reason, and determining the
perceived popularity for the musical artist based on the perceived
popularity for each of the songs, the perceived popularity for each of
the musical albums and the perceived popularity for the musical artist.

[0014] The preliminary perceived popularity for the musical artist alone
may be based on popularity of the musical artist without regard to
popularity of individual songs or musical albums related to the musical
artist. The non-music related reason may include activism, acting,
gossip, interpersonal relationships and/or tragedy. The individual
attributes related to the non-music related reason may include a cause,
an organizational affiliation, a television show, a movie, a commercial,
a tabloid article, a famous significant other, a famous relative and/or
an accident.

[0015] Implementations of any of the techniques described may include a
method or process, an apparatus or system, or computer software on a
computer-accessible medium. The details of particular implementations are
set forth below. Other features will be apparent from the description and
drawings, and from the claims.

DESCRIPTION OF DRAWINGS

[0016]FIG. 1A is an exemplary communications system for providing search
results based on popularity of content items associated with the search
results.

[0017]FIG. 1B is a flow chart of an exemplary process for providing
search results based on popularity of content items associated with the
search results.

[0018]FIG. 1c is a flow chart of an exemplary process for providing
search results based on popularity of content items associated with the
search results in response to a search query known to be related to a
particular genre.

[0019] FIG. 2 is a flow chart of an exemplary process for identifying a
content item associated with a search result.

[0020]FIG. 3 is a flow chart of an exemplary process for associating a
generated perceived popularity for a content item with the content item
and storing the association.

[0021] FIG. 4 is a flow chart of an exemplary process for generating a
perceived popularity for a content item.

[0022]FIG. 5 is a flow chart of an exemplary process for generating a
perceived popularity for a song, associating the perceived popularity
with the song, and storing the association in a genre-specific catalog.

[0023]FIG. 6 is a flow chart of an exemplary process for generating a
perceived popularity for a music album associating the perceived
popularity with the music album, and storing the association in a
genre-specific catalog.

[0024] FIG. 7 is a flow chart of an exemplary process for generating a
perceived popularity for a music artist, associating the perceived
popularity with the music artist, and storing the association in a
genre-specific catalog.

[0025]FIG. 8 is an illustration of a data file included in a
genre-specific catalog.

[0026]FIG. 9 is an illustration of a graphical user interface (GUI)
configured to provide search results for a search query based on
popularity of songs associated with the search results.

[0027] FIGS. 10-12 are illustrations of GUIs configured to provide video
search results based on popularity of songs associated with the video
search results.

DETAILED DESCRIPTION

[0028] When a user is interested in finding information about a real-world
person or item on the Internet, the user submits a search query to a
search engine or system. The search engine or system presents search
results that correspond to the search query, each search result including
a link or pointer selectable by the user to access a digital instance
(e.g., a web page) that includes information that is deemed to be
responsive to the user's information need, as represented by the
user-inputted search query. The presentation of the search results to the
user may be enhanced, particularly for users seeking audio and/or video
(referred to as audio/video) information, by modifying the presentation
of the search results based on a perceived popularity of the real-world
person or item.

[0029] A real-world person or item may be referred to as a content item.
Examples of content items include a band, a musical artist, a musical
album or a song. A digital instance may be a digital asset, such as, for
example, a digital audio file, a digital video file, or a web page that
describes a content item (e.g., a web page that includes information
about a musical artist) or embodies the content item (e.g., a digital
audio file that embodies a song). A content item may be identified as
being associated with a search result if the digital instance referred to
by the search result describes or embodies the content item.

[0030] A perceived popularity for a content item may be one or more
parameters that indicate a degree of popularity for the content item in
comparison with other content items. Notably, a perceived popularity for
a content item is distinct from a popularity of a digital instance. In
particular, a perceived popularity is a popularity measure for a
real-world person or item, which may be described by or embodied in a
digital asset. A popularity of the digital instance, in contrast, is a
popularity measure of the digital asset itself. For example, the singer
Madonna (i.e., content item) is extremely popular world-wide; however,
web pages that include information about Madonna may vary in popularity
depending on factors other than the popularity of Madonna, herself (e.g.,
quality of information provided in the web page, number of links to the
web page and notoriety of the web page authors). Consequently, assessment
of the popularity of Madonna (e.g., content item) of a relatively new or
obscure web page that references Madonna may be used as a basis for
increasing a rank for that web page relative to other web pages that
reference less popular content items (e.g., the 1980s singer Tiffany),
even if those other web pages are themselves historically more frequently
accessed by web users.

[0031] Search results associated with content items having higher
perceived popularity may be deemed to be more likely responsive to a
user's information need than search results associated with content items
having lower perceived popularity. This may be the case because a search
result that is associated with a more popular content item (e.g., more
popular subject matter) is more likely to be responsive to a user's
search query than a search result that is associated with a less popular
content item. The search results deemed more likely to be responsive to
the user's information need may be preferentially presented in a display
over search results deemed less likely to be responsive to the user's
information need.

[0032] In one implementation, search results associated with content items
having a relatively high perceived popularity may be preferentially
presented to the user by presenting the search results higher in a ranked
list than search results associated with content items having a
relatively low perceived popularity. For example, search results
associated with Madonna, the nationally recognized singer of such hits as
"Lucky Star", may be ranked higher than search results associated with
Madonna, a lesser-known cover singer in Chicago.

[0033] A perceived popularity for a content item may be gleaned from
searching, or crawling, the Internet using, for example, focus asset type
crawling. The number of references to a particular content item may be
used as an indicator of the popularity of the content item, and
therefore, may be used to generate a perceived popularity for the content
item. The search of the Internet may be improved by leveraging databases
that include information related to a particular type or genre of content
items for which perceived popularity are desired. For example, if the
content items relate to music (e.g., bands, artists, albums and/or
songs), music-related databases, such as, for example, Muse, FreeDB and
All Music Guide (AMG), may be accessed to increase the reliability of
detection of music-related references during the search of the Internet
by increasing the chances that a detected reference to a music content
item on the Internet is actually a reference to a music content item and
not a reference to a content item that belongs to another genre. For
example, a search of the Internet for references to the singer Madonna
may be supplemented with information included in a music-related database
by adjusting the search query to include additional information related
to Madonna the musician, such as, for example, generic music-related
words (e.g., song, music, lyric), Madonna album titles (e.g., "Ray of
Light," "True Blue" and "Like a Prayer"), and Madonna song titles (e.g.,
"Lucky Star," "Borderline" and "Holiday"). Additionally, or
alternatively, a query related to the singer Madonna may be supplemented
with non-music related words (e.g., words associated with religious
references to the Madonna) and an indication to stay away from those
words. In this way, references related to the word "Madonna" may be
identified if they are music-related references, rather than
religious-related references to the Madonna.

[0034] In addition to generating perceived popularity for music-related
content items, the same or analogous techniques described herein also may
be applied, for example, to generating perceived popularity for
sports-related content items (e.g., sports figures, teams or particular
games), news-related content items (e.g., news programs, newspapers or
anchors), celebrity-related content items (e.g., movies, television shows
or actors) and/or politics-related content items (e.g., political issues,
races or candidates/politicians), to name just a few. In some
implementations, the techniques described herein may be used to enhance
the presentation of search results associated with any type of
information that is stored in any local or remote location, if the search
results may be associated with content items that are referenced on the
Internet such that a perceived popularity associated with the content
items may be generated.

[0035] Communications system 100A of FIG. 1A includes a client 110 and a
search system 130 that communicate through a network 120 to provide
search results based on popularity of content items associated with the
search results. A content item may be associated with a search result if
the search result provides a link for accessing a digital instance that
describes or embodies the content item.

[0036] Each of the client 110 and the search system 130 may be implemented
by, for example, a general-purpose computer capable of responding to and
executing instructions in a defined manner, a personal computer, a
special-purpose computer, a workstation, a server, a device, a component,
other equipment or some combination thereof capable of responding to and
executing instructions. The client 110 and search system 130 may be
configured to receive instructions from, for example, a software
application, a program, a piece of code, a device, a computer, a computer
system, or a combination thereof, which independently or collectively
direct operations, as described herein. The instructions may be embodied
permanently or temporarily in any type of machine, component, equipment,
storage medium, or propagated signal that is capable of being delivered
to the client 110 or the search system 130.

[0037] The client 110 may include one or more devices capable of accessing
content on the search system 130. The search system 130 may include a
general-purpose computer (e.g., a personal computer (PC)) capable of
responding to and executing instructions in a defined manner, a
workstation, a notebook computer, a PDA ("Personal Digital Assistant"), a
wireless phone, a component, other equipment, or some combination of
these items that is capable of responding to and executing instructions.

[0038] In one implementation, the client 110 includes one or more
information retrieval software applications (e.g., a browser, a mail
application, an instant messaging client, an Internet service provider
client, a media player, or another integrated client) capable of
receiving one or more data units. The information retrieval applications
may run on a general-purpose operating system and a hardware platform
that includes a general-purpose processor and specialized hardware for
graphics, communications and/or other capabilities. In another
implementation, the client 110 may include a wireless telephone running a
micro-browser application on a reduced operating system with general
purpose and specialized hardware capable of operating in mobile
environments.

[0039] The network 120 includes hardware and/or software capable of
enabling direct or indirect communications between the client 110 and the
search system 130. As such, the network 120 may include a direct link
between the client 110 and the search system 130, or it may include one
or more networks or sub networks between them (not shown). Each network
or sub network may include, for example, a wired or wireless data pathway
capable of carrying and receiving data. Examples of the delivery network
include the Internet, the World Wide Web, a WAN ("Wide Area Network"), a
LAN ("Local Area Network"), analog or digital wired and wireless
telephone networks, radio, television, cable, satellite, and/or any other
delivery mechanism for carrying data.

[0040] The search system 130 may include a general-purpose computer having
a central processor unit (CPU), and memory/storage devices that store
data and various programs such as an operating system and one or more
application programs. Other examples of a search system 130 includes a
workstation, a server, a special purpose device or component, a broadcast
system, other equipment, or some combination thereof capable of
responding to and executing instructions in a defined manner. The search
system 130 also may include an input/output (I/O) device (e.g., video and
audio input and conversion capability), and peripheral equipment such as
a communications card or device (e.g., a modem or a network adapter) for
exchanging data with the network 120.

[0041] The search system 130 is generally capable of executing
instructions under the command of a controller. The search system 130 may
be used to provide content to the client 110. The controller may be
implemented by a software application loaded on the search system 130 for
commanding and directing communications exchanged with the client 110.
Other examples of the controller include a program, a piece of code, an
instruction, a device, a computer, a computer system, or a combination
thereof, for independently or collectively instructing the client 110 or
the search system 130 to interact and operate as described. The search
system 130 may be embodied permanently or temporarily in any type of
machine, component, physical or virtual equipment, storage medium, or
propagated signal capable of providing instructions to the client 110 or
the search system 130.

[0042] Process 100B of FIG. 1B is configured to provide search results
based on popularity of content items associated with the search results
when a search query is not known to be associated with a specific genre.
For convenience, particular components described with respect to FIG. 1A
are referenced as performing the process 100B. However, similar
methodologies may be applied in other implementations where different
components are used to define the structure of the system, or where the
functionality is distributed differently among the components shown by
FIG. 1A.

[0043] The client 110 receives a search query from a user (151) and sends
the search query to the search system 130 through the network 120 (152).
In the implementation shown in FIG. 1B, the search query is provided by
the user in such a way that it is not known to be associated with a
particular category or topic, referred to as a "genre." For example, the
search query may be inputted into a search engine that is not a
genre-specific search engine, or may be inputted into a search engine
without the user providing an indication of an associated genre.

[0044] The search system 130 receives the search query from the client
(153) and determines search results based on the search query (154). The
search system 130 identifies search results that are related to a genre
(155). The genre may be determined based on information provided by the
user in conjunction with the search query, based on a categorization of a
search result or a digital instance referred to by the search result, or
by some other means. For example, 5 search results may be determined,
where 2 of the search results are related to a "music" genre, 2 of the
search results are related to a "nature" genre, and 1 of the search
results is related to a "sports" genre. The search system 130 may
determine that a particular search result is related to a genre by
accessing information, such as metadata, associated with a digital
instance referred to by the search result. For example, a search result
may refer to a web page entitled "I Love Duran Duran" and the web page
may be associated with metadata that includes keywords, such as, for
example, "music," "rock," and "1980s" included in the web page. The
search system 130 may access the keywords included in the metadata and,
based on, for example, an ontology or taxonomy, identify a genre to which
the keywords belong. In the present example, the keywords "music,"
"rock," and "1980s" may be determined to belong to a music genre.

[0045] In operation 155, the search system 130 identifies search results
that are related to a single genre--thus, and for example, search system
130, at this time, identifies the two search results that are related to
the "music" genre.

[0046] The search system 130 identifies one or more content items
associated with each of the genre-related search results based on a
genre-specific catalog (156). Content items that may be associated with a
search result may be included in a catalog that is specific to the genre
related to the search result. The genre of the genre-specific catalog may
be the single genre identified for the search results in operation 155
described above. In the present example, the genre identified as being
related to the two search results is "music," and thus, the catalog
accessed by the search system 130 in order to identify content items
associated with each of the genre-related search results is a
music-specific catalog.

[0047] As described above, a content item may be a real-world person or
item, such as, for example, a band, a musical artist, a musical album or
a song, and a digital instance may be a digital asset, such as, for
example, a digital audio file, a digital video file, or a web page. In
the implementation in which content items are music-related (e.g., bands,
artists, musical albums and songs), a set of bands, artists, musical
albums and songs may be identified during a preliminary search of the
Internet and/or by accessing music-related databases. More particularly,
the Internet may be searched or crawled to identify a set of
music-related content items based on a search for generic music-related
words (e.g., song, lyric and music) or music-related words and concepts
related to a particular content item (e.g., band names, artist names,
album names or song names). The set of music-related content items may be
stored in a music-specific catalog.

[0048] Additional information related to the content items stored in a
genre-specific catalog, such as, for example, perceived popularity for
the content items, also may be determined and stored in the
genre-specific catalog. As such, a second search of the Internet may be
carried out for each identified content item to identify references to
the content item. A reference to a content item on the Internet may
include information related to the content item described by, or embodied
within, a digital instance (e.g., text in a web page that states "Madonna
rocks," an audio file that includes the song "Holiday," or an image of
the cover art from the album "True Blue"). To determine whether content
on a web page or within another type of digital instance (e.g., an image,
an audio file, a video file or a document) includes a reference to a
music-related content item, the digital instance may be analyzed using a
classifier (e.g., a classifier based on a machine learning algorithm,
such as, for example, the Support Vector Machine (SVM) algorithm, the
Bayes algorithm or the Perceptron algorithm). The classifier may classify
web pages or other digital instances as being in a music category (or a
broader category, such as, for example entertainment) or being in some
other category. The digital instances that are classified as music
digital instances, and include content that relates to a music-related
content item for which the Internet is being searched, may be used to
determine a perceived popularity of the content item, which may be stored
in the genre-specific catalog in association with the content item, as
described below.

[0049] A content item may be identified as being associated with a search
result if the digital instance referred to by the search result describes
the content item or embodies the content item. To determine if a
particular genre-related search result is associated with a content item
that is included in the genre-specific catalog, the digital instance (or
information related thereto, such as, for example, metadata) to which the
particular genre-related search result refers may be accessed and
compared with one or more of the content items included in the
genre-specific catalog. If the digital instance includes information that
is the same as, or sufficiently similar to, information associated with a
content item, the digital instance may be deemed to describe or embody
the content item.. For example, a search result that refers to a web page
(i.e., digital instance) that provides lyrics to the song "Big Yellow
Taxi" by the band the Counting Crows," may be deemed to be associated
with a song (i.e., content item) entitled "Big Yellow Taxi." The search
system 130 assesses perceived popularity for the identified content items
(157). The genre-specific catalog may include a perceived popularity for
each entry associated with a content item. As described above, a
perceived popularity may be a parameter (e.g., a single number or other
value) that indicates how popular a content item is on the Internet,
where the parameter may have a high value if the content item is popular
and a low value if the content item is not popular (or vice versa). For
each content item determined to be associated with the identified
genre-related search results, the search system 130 accesses a perceived
popularity stored in the genre-specific catalog. In some implementations,
the perceived popularity may be determined by the search system 130
on-the-fly based on the same method (e.g., process 300 of FIG. 3) used to
populate the genre-specific database.

[0050] The search system 130 determines a presentation of the search
results based on the perceived popularity (158). A presentation may, for
example, be a visual list of search results, a list of search results
that are presented aurally, or a collection of search results presented
in a manner other than a list In some implementations, search results
identified as being related to the determined genre (i.e., genre-related
search results) may be presented in a way that is visually distinct
(e.g., at the top of a search result list, under a heading or grouped
together) from search results that are not related to the genre (i.e.,
genre-neutral search results). Alternatively, all search results may be
provided together as a fused group without any indication or separation
of genre-related search results versus genre-neutral search results.

[0051] In either case, genre-related search results may be further
organized based on the perceived popularity for the content items
associated with the identified search results. For example, search
results associated with content items having perceived popularity
indicative of greater popularity may be presented higher in a search
result list, at a privileged position (e.g., grouped to the side of the
search result list) or with a specialized indicator (e.g., a high
popularity star next to each search result) than those search results
associated with content items having perceived popularity that are
indicative of lesser popularity. Furthermore, genre-neutral search
results may be presented, for example, as a group that is visually
separated from the genre-related search results. Alternatively, and also
for example, non-genre-related search results may be intermixed with
genre-related search results, such that the placement of the
genre-related search results relative to each other changes based on the
perceived popularity, but the position of the genre-related search
results remains the same relative to the non-genre-related search
results.

[0052] The search system 130 provides the search results and presentation
to the client through the network 120 (159). The client 110 displays the
search results to the user based on the determined presentation (160).

[0053] In some implementations, the perceived popularity stored in the
genre-specific catalog also may be used by the search system 130, for
example, to identify digital instances for automatic placement in a
content (e.g., audio or video) inbox or to provide recommended content
and targeted advertisements to a user.

[0054] In some implementations, the client 110 may request direct access
to a genre-specific or a genre-neutral catalog stored in data store 140
from search system 130 through network 120. The direct access may enable
the client 110 to use the perceived popularity stored within the catalog
for content items to organize digital instances (that describe or embody
those content items) within a private database, and provide
popularity-based search results when querying the private database.

[0055] Process 100C of FIG. 1c is configured to provide search results
based on popularity of content items associated with search results in
response to a search query that is known to be associated with a
particular genre. For convenience, particular components described with
respect to FIG. 1A are referenced as performing the process 100C.
However, similar methodologies may be applied in other implementations
where different components are used to define the structure of the
system, or where the functionality is distributed differently among the
components shown by FIG. 1A.

[0056] The client 110 receives a genre-related search query from a user
(171) and transmits the search query to the search system through the
network 120 (172). The search system 130 may determine that the search
query is related to a genre, in some implementations, because the search
query was provided by the user into a genre-specific search engine, such
as, for example, a music-based search engine. Alternatively, or
additionally, the user may provide, or select, a genre to be associated
with the search query upon providing the search query to a search engine.
For example, the user may select a genre from a pull-down menu or some
other user interface element, or the user may supply their own genre via
an input box. A search query related to the "music" genre may include
lyrics for a song, a full or partial title of a song, a full or partial
title of a music album, an artist name or any combination thereof. For
example, a search query received by search system 130 may be "paved
paradise," which represents a portion of a lyric from the song "Big
Yellow Taxi."

[0057] The search system receives the genre-related search query from the
client (173) and determines search results based on the genre-related
search query (174), as described above with respect to process 100B of
FIG. 1B. In the implementation of process 100C, the search system 1300
need not necessarily determine one or more genres associated with the
search results because the search query is already related to a genre. As
such, and presumably, the search results determined based on the
genre-related search query already may be associated with a known genre
(e.g., the genre of the genre-specific search query).

[0058] The search system 130 identifies a content item associated with
each of the genre-related search results based on a genre-specific
catalog (175). Operation 175 may be performed in the same manner as that
described for operation 156 of process 100B, and as further described
below with respect to FIG. 2.

[0059] The search system 130 assesses perceived popularity for the
identified content items (176). In some implementations, the perceived
popularity may be stored in, and accessed from, a genre-specific catalog
that is related to the genre of the genre-specific search query, or may
be determined on-the-fly, as described above with respect to process 100B
of FIG. 1B. Alternatively, the perceived popularity may be stored in a
genre-neutral catalog for content items, and which includes an indication
of a genre associated with at least some of the content items stored in
the genre-neutral catalog. For example, a genre-neutral catalog may
include two entries for a content item "Eagles," where one entry includes
an indication of a "music" genre and the other includes an indication of
a "sports" genre, and each entry is associated with its own perceived
popularity.

[0060] The search system 130 determines a presentation of the search
results based on the perceived popularity (177) and provides the search
results and presentation to the client 110 (178). The client 110 displays
the search results to the user based on the presentation (179).

[0061] In some implementations, search results determined during processes
100B or 100C may not be identified as being related to a particular
genre. As such, a genre-specific catalog may not be accessed by the
search system 130 to identify content items associated with each of the
determined search results. Instead, the search system 130 may access a
genre-neutral catalog, which may include a listing of content items
related to multiple genres (and, in some cases, an indication of genres
to which the stored content items are related), as well as perceived
popularity related to each of the stored content items. Each search
result determined based on the search query may be compared against the
genre-neutral catalog to identify a content item that is associated with
the search results, as described in more detail with respect to FIG. 2.

[0062] Process 200 of FIG. 2 is configured to identify a content item
associated with a search result. For convenience, particular components
described with respect to FIG. 1A are referenced as performing the
process 200. However, similar methodologies may be applied in other
implementations where different components are used to define the
structure of the system, or where the functionality is distributed
differently among the components shown by FIG. 1A. Process 200 is one
exemplary implementation of operation 156 of FIG. 1B and operation 176 of
FIG. 1c.

[0063] The search system 130 selects a genre-related search result (210).
The search results were determined, and identified as being related to a
particular genre, during at least one of processes 100B and 100C. For
example, the search system 130 selects a search result that refers to a
Madonna Blog web page (i.e., digital instance) that discusses the singer
Madonna.

[0064] A search result includes a link (i.e., hyperlink) or pointer that
is selectable by a user to access a digital instance that is referred to
by the search result. A search result also includes non-link based
information that describes the digital instance to which the search
result refers. For example, the Madonna search result includes a link to
the Madonna Blog web page (i.e., www.madonnablog.com) and non-link based
information about the web page (i.e., web page title "Stephanie's Madonna
Blog" and the first sentence displayed on the web page "Welcome to my
Madonna blog!").

[0065] The search system 130 accesses non-link based information for the
search result (220). In the present example, the search system 130
accesses the non-link based information for the Madonna search result,
which includes the web page title "Stephanie's Madonna Blog" and the
first sentence of the web page "Welcome to my Madonna blog!"

[0066] The search system 130 accesses the digital instance to which the
search result refers using link-based information for the search result
(230). In the present example, the search system 130 accesses the Madonna
Blog web page by following the link-based information for the search
result (e.g., by following the link www.madonnablog.com).

[0067] The search system 130 compares words and phrases included in the
non-link based information and content of the digital instance with
content items stored in the genre-specific catalog (240). For example,
the genre-specific catalog includes the content items "Madonna,"
"Prince," and "U2." The search system 130 compares these content items
(and any information associated therewith in the genre-specific catalog,
such as, for example, other keywords that are associated with the content
items) with words and phrases gleaned from the non-link based information
for the search result and content of the Madonna Blog web page.

[0068] The search system 130 identifies candidate content items based on
matches between the words and phrases and the stored content items (250).
For example, the words and phrases in the non-link based information for,
and content within, the Madonna Blog web page include the word "Madonna,"
which is a match for the content item "Madonna." As such, the content
item "Madonna" is identified as a candidate content item. In some
instances where the words and phrases are a match for more than one
content item (e.g., a web page related to a Prince vs. Madonna party may
include words and phrases that are a match for both the content item
"Madonna" and the content item "Prince"), more than one candidate content
item may be identified.

[0069] The search system 130 determines if more than Z candidate content
items have been identified or less than 1 candidate content item has been
identified (260). The number Z may be a predetermined or user-defined
threshold number of content items. If more than the threshold number of
content items have been identified as candidate content items, it may not
be possible for the search system to accurately determine that a
particular search result is truly associated with a useful number of
content items. For example, a web page that includes an article about
musicians from the 1980s may include words and phrases that are a match
for 25 content items stored in the genre-specific catalog. As such,
although the web page is associated with a large number of content items,
the web page may not include enough detailed information about a useful
number of those content items to be deemed to be associated with any one
particular content item. Similarly, if less than 1 candidate content item
(i.e., zero candidate content items) has been identified, the selected
search result cannot be said to be associated with a particular content
item.

[0070] As such, if less than 1, or more than Z, candidate content items
have been identified, the search system 130 determines that the selected
search result is not associated with one or more content items (270).

[0071] If more than 1, or less than Z, candidate content items have been
identified, the search system 130 associates the selected search result
with the identified candidate content items (280). If the number of
candidate content items falls between 1 and the threshold number Z, the
search system 130 may determine that the selected search result is
associated with a useful number of content items, and may so associate
those content items with the selected search result.

[0072] Process 300 of FIG. 3 is configured to generate a perceived
popularity for a content item and store the association between the
perceived popularity and the content item in the genre-specific catalog.
For convenience, particular components described with respect to FIG. 1A
are referenced as performing the process 300. However, similar
methodologies may be applied in other implementations where different
components are used to define the structure of the system, or where the
functionality is distributed differently among the components shown by
FIG. 1A.

[0073] The search system 130 accesses a genre-specific catalog and selects
a content item, from among content items stored within the genre-specific
catalog, to determine if the content item is popular (310). The
genre-specific catalog may include content items related to a particular
category, such as, for example, music, sports, news, movies or
television. The content items that may be included in a music-related
catalog, for example, may be artists and related songs, music videos and
albums. The search system 130 may select a content item in operation 310
if, for example, the content item does not have a perceived popularity
associated therewith or a perceived popularity associated with the
content item is stale (e.g., the perceived popularity has not been
updated for longer than a threshold amount of time).

[0074] The search system 130 forms a popularity query for the selected
content item based on information associated with the content item and
stored in the catalog (320). For example, the song "Holiday" (i.e., a
content item) may be selected, and the popularity query for the song may
include the title of the song (e.g., "Holiday") as well as other
information associated with the song (e.g., artist "Green Day" and album
"American Idiot"). Thus, in the present example, the popularity query may
be "Holiday AND Green Day AND American Idiot" or some combination, or
sub-combination thereof.

[0075] In another implementation, the search system 130 forms a popularity
query for the selected content item based on information stored in other
data sources, in addition to, or instead of, the genre-specific catalog.
For example, the search system 130 may form a popularity query for the
song "Holiday" based on information found in a music web page (e.g.,
CDNow.com) about songs having the title "Holiday."

[0076] The popularity query for a content item may be used by the search
system 130 to determine if information related to the content item is
being referenced on the Internet, and thus, whether, and to what extent,
the content item is popular. To do so, the search system 130 searches
private (e.g., a hard drive of a personal computer) and public (e.g., the
Internet) networks for digital instances that satisfy the popularity
query (330). For example, the search system 130 may review web pages on
the Internet seeking information that is related to the popularity query,
much like a search engine may traverse the Internet seeking information
that satisfies a search query. For example, search system 130 may detect
a web page that is entitled "Green Day Rocks" and which refers to both
the song "Holiday" and the album "American Idiot."

[0077] The search system 130 determines popularity search results based on
the digital instances identified as satisfying the popularity query
(340). More particularly, the search system 130 may generate search
results that refer to digital instances that describe or embody the
selected content item (e.g., the "Green Day Rocks" web page). The search
results may be used by the search system 130 to determine the popularity
of the content item.

[0078] The search system 130 generates a perceived popularity for the
content item based on the popularity search results (350). For example,
the more popularity search results that are determined, the more popular
the content item may be, as described in more detail below.

[0079] The generated perceived popularity may be associated with the
selected content item and stored within the genre-specific catalog (360)
in association with the content item in, for example, a perceived
popularity field. For example, an entry within the genre-specific catalog
may include the song "Holiday" and a number that represents a perceived
popularity associated with the song. The search system 130 may refer to
the perceived popularity field of the genre-specific catalog whenever
search results that are related to the genre are to be presented to a
user.

[0080] In some implementations, the popularity field of the genre-specific
catalog may be referenced by a device or system outside search system
130. For example, a user may maintain a private database of digital
instances related to a particular genre (e.g., music), which describe or
embody particular content items. The user may wish to sort or search the
private database based on popularity of the content items which are
described by or embodied in the digital instances. As such, the user's
local device (e.g., personal computer) may contact the search system 130
through network 120 and request access to the genre-specific catalog as
stored in data store 140. The local device then may determine if a
perceived popularity exists for content items that are the same as, or
similar to, content items described by or embodied in digital instances
stored in the private database. If so, the local device may identify the
perceived popularity and associate them with content items (via the
digital instances) in the private database.

[0081] In some implementations, the popularity field of the genre-specific
catalog may be referenced by search system 130 for providing information
other than search results. For example, search system 130 may provide
information to a user in a content (e.g., video or audio) inbox. Upon
entering the inbox, the user may be presented with "hot" digital
instances. The "hot" digital instances may be determined, and presented
to the user, based on perceived popularity stored in the catalog, and
associated with the "hot" digital instances describing or embodying
popular content items. In another example, the same perceived popularity
can be used to identify, for example, information recommendations and
advertisements for a user.

[0082] Process 400 of FIG. 4 is configured to generate a perceived
popularity for a content item. For convenience, particular components
described with respect to FIG. 1A are referenced as performing the
process 100B. However, similar methodologies may be applied in other
implementations where different components are used to define the
structure of the system, or where the functionality is distributed
differently among the components shown by FIG. 1A. Process 400 is an
illustration of one implementation of operation 350 of FIG. 3.

[0083] The search system 130 receives popularity search results for a
particular content item, as determined in process 300 of FIG. 3 (410).
The popularity search results may include search results determined by
the search system 130 based on the popularity query formed during process
300 of FIG. 3.

[0084] The search system 130 determines that N popularity search results
are received, where N is referred to as the raw popularity score (420).
The search system 130 may simply count the number of popularity search
results retrieved in response to the popularity query, or the search
system 130 may increase a counter each time a popularity search results
is determined. Thus, upon completion of the determination of all
responsive popularity search results, the search system 130 may determine
that the list of responsive popularity search results includes N
popularity search results. For example, if 20 popularity search results
are received, N=20.

[0085] The search system 130 uses a classifier to analyze the top T
popularity search results from among the N popularity search results,
where T is less than N (430). A popularity search result may be among the
top T popularity search results if the search result is associated with a
greater perceived popularity than a predetermined number of other search
results. The number T may be predetermined or set by a user.

[0086] In general, a classifier is a decision system that is provided with
values of some features or characteristics of a situation as input and
produces as an output a discrete label related to the input values. A
classifier may be a machine learning classifier in that the classifier
automatically builds upon the initial input values based on data
encountered by the classifier, and, in some situations, feedback provided
by a user, in order to provide more accurate results (e.g., labeling) in
the future.

[0087] The classifier used by search system 130 may be seeded with
information related to the content item selected. For example, if the
content item selected is a song, the classifier may include information
related to songs in order for the classifier to determine if any of the
top T popularity search results are related to music, in general, and
songs, in particular. The classifier may include song-related
information, such as, for example, song titles, artist names, album
titles, file extensions associated with music (e.g., .mp3 or .wav) and
other music-related words (e.g., "band," "track," "CD" and "concert").

[0088] The classifier determines R popularity search results (from among
the top T popularity search results) that are related to the genre of the
genre-specific catalog (e.g., music) from which the content item (e.g.,
song) was selected (440). For example, the top T popularity search
results for a song entitled "Breathe" may include search results related
to the song "Breathe" by artist Faith Hill, the song "Breathe" by artist
"Blu Cantrell" and articles related to new products for helping people
"breathe" better at night. Thus, the classifier may include the
popularity search results for the two songs entitled "Breathe" within the
R popularity search results, while the article related to better
breathing may not be included.

[0089] The classifier determines an ambiguity ratio to indicate a number
of popularity search results that are related to the genre in relation to
the number of popularity search results that are analyzed by the
classifier (450). For example, the ratio may be the R popularity search
results related to the music genre over the T top search results that
were analyzed to determine search results within the music genre (i.e.,
R/T). The ratio may be referred to as an ambiguity score. Additionally,
or alternatively, the classifier may determine a number of popularity
search results N and a number of popularity search results R (from the
entire group of N popularity search results) that are within the genre
from which the content item was selected. In this implementation, the
ambiguity score may be determined by calculating the logarithm of the
ratio N/R (i.e., log (N/R)).

[0090] The search system 130 determines a perceived popularity for a
content item based on the raw popularity score and the ambiguity score
(460). For example, the perceived popularity (e.g., a single popularity
score) may be determined by performing an operation (e.g., addition,
subtraction, multiplication or division) on the raw popularity score and
the ambiguity score. For example, the following formula may be used.

BP(e)=N(e)+AR(e)

where BP(e) is the perceived popularity for a content item e, N(e) is the
raw popularity score for the content item e and AR(e) is the ambiguity
ratio for the content item e.

[0091] Process 500 of FIG. 5 is configured to determine a perceived
popularity for a song, associating the perceived popularity with the
song, and storing the association in a genre-specific catalog. For
convenience, particular components described with respect to FIG. 1A are
referenced as performing the process 500. However, similar methodologies
may be applied in other implementations where different components are
used to define the structure of the system, or where the functionality is
distributed differently among the components shown by FIG. 1A.

[0092] The search system 130 accesses a music-specific catalog and selects
a song to determine if the song is popular (510). The content item
selected from the music-specific catalog can also be an artist, a music
video or an album. For example, the search system 130 may select a song
entitled "Big Yellow Taxi."

[0093] The search system 130 forms a popularity query for the song based
on various combinations of tetins related to information in the
music-specific catalog related to the song (520). For example, the
popularity query may be "song," "song and artist," "song and CD1, "song
and artist and CD1," "song and CD2" and "song and artist and CD2," where
CD1 is a first album on which the song appears and CD2 is a second album
on which the song appears, such as, for example, a soundtrack or greatest
hits album. In the present example, the popularity query for "Big Yellow
Taxi" may be "song and artist"--"Big Yellow Taxi AND Counting Crows."

[0094] The search system 130 searches both public (e.g., the Internet) and
private (e.g., locally-stored) networks for content that satisfies the
popularity query (530) and determines popularity search results based on
the content (540). The popularity search results may refer to documents
(e.g., web pages, text documents, audio, video and images) that include
content that satisfies the popularity query. For example, a popularity
search result for the popularity query "Big Yellow Taxi AND Counting
Crows" may refer to a web page entitled "Lyrics for Big Yellow Taxi by
Counting Crows."

[0095] The search system 130 determines a perceived popularity for the
song based on the popularity search results (550), as described above.
The perceived popularity may be associated with the selected content item
and stored within the genre-specific catalog (560). For example, a
particular perceived popularity may be stored in the genre-specific
catalog in association with an entry for the song "Big Yellow Taxi."

[0096] Process 600 of FIG. 6 is configured to determine a perceived
popularity for a music album associating the perceived popularity with
the music album, and storing the association in a genre-specific catalog.
For convenience, particular components described with respect to FIG. 1A
are referenced as performing the process 600. However, similar
methodologies may be applied in other implementations where different
components are used to define the structure of the system, or where the
functionality is distributed differently among the components shown by
FIG. 1A.

[0097] In general, the popularity of an album (e.g., a compact disc (CD))
may be determined based on a combination of the popularity of the album
by itself and the popularity of individual songs on the album. To
determine the popularity of a CD, the search system 130 receives an
indication that a CD is the selected content item for which popularity is
to be determined (610). For example, upon selection of the content item
from the catalog by the search system 130, the search system 130 may
detect a tag or other indicia associated with the content item, which
indicates that the selected content item is a CD, rather than a song or
artist.

[0098] The search system 130 identifies songs that are on the CD (620). In
some implementations, an indication of the songs on the CD may be
associated with the CD content item within the genre-specific catalog.
For example, the CD "Breathe" by artist Faith Hill may include, inter
alia, the songs "What's In It For Me?," "I Got My Baby," and "Breathe."
Additionally, or alternatively, search system 130 may refer to another
source (e.g., a music-related database, such as, for example, Muse,
FreeDB or AMG) to identify songs that are on a particular CD.

[0099] The search system 130 determines a perceived popularity for each
song on the CD 630), using, for example, process 500 of FIG. 5. The
search system 130 determines a preliminary perceived popularity for the
CD, which includes only the popularity of the CD on its own (i.e.,
without the influence of popularity of any particular song on the CD)
(640).

[0100] The search system 130 determines a perceived popularity for the CD
based on the perceived popularity for each song and the preliminary
perceived popularity for the CD (650). In some implementations, to
combine a perceived popularity for songs on a CD with a preliminary
perceived popularity of the CD, itself, a mathematical operation (e.g.,
addition, subtraction, multiplication, division or averaging) may be
performed on the perceived popularity.

[0101] Process 700 of FIG. 7 is configured to determine a perceived
popularity for a music artist, associating the perceived popularity with
the music artist, and storing the association in a genre-specific
catalog. For convenience, particular components described with respect to
FIG. 1A are referenced as performing the process 700. However, similar
methodologies may be applied in other implementations where different
components are used to define the structure of the system, or where the
functionality is distributed differently among the components shown by
FIG. 1A.

[0102] Similar to that described above, in general, the popularity of an
artist may be determined based on a combination of the popularity of
songs and CDs produced by the artist, as well as personal popularity of
the artist related to appearances by the artist in television
commercials, television shows or movies, activism performed by the artist
or any celebrity gossip related the artist.

[0103] To determine the popularity of an artist, the search system 130
receives an indication that an artist is the selected content item for
which popularity is to be determined, similar to the determination made
in operation 610 of FIG. 6 (710). The search system 130 identifies songs
by the artist (720) and CDs by the artist, similar to the identification
made in operation 620 of FIG. 6 (730).

[0104] The search system 130 determines if the artist is popular for
reasons other than their music career (e.g., acting) and determines other
attributes that are related to the other reason (e.g., movie or
television roles) (740). For example, the music-related catalog may
include information related to personal popularity for an artist, and the
search system 130 may, in one implementation, request this information
from the catalog along with, or subsequent to, selection of the artist
from the catalog. In some implementations, the search system 130 may
access other sources, such as, for example, other artist and celebrity
information databases and web pages (e.g., E Online! web page or
Entertainment Tonight web page) to identify information related to
personal popularity for a particular artist.

[0105] The search system 130 determines a perceived popularity for each
song by the artist (750), using, for example, process 500 of FIG. 5, and
each CD by the artist, using, for example, process 600 of FIG. 6 (760).

[0106] The search system 130 also determines a perceived personal
popularity for the artist based on musical reasons (e.g., a Grammy win)
and other reasons (e.g., having a baby or being in the summer blockbuster
movie) (770).

[0107] The search system 130 determines a perceived popularity for the
artist based on the perceived popularity for each song and each CD and
the perceived personal popularity for the artist (780). In some
implementations, to combine a perceived popularity for songs on a CD,
various CDs and a perceived personal popularity for an artist, a
mathematical operation (e.g., addition, subtraction, multiplication,
division or averaging) may be performed on the perceived popularity.

[0108] Data file 800 of FIG. 8 is included in data store 140. More
particularly, data file 800 is an illustration of a music-specific
catalog 800. For illustrative purposes, music-specific catalog 800 shows
content items that are songs, where each entry in the music-specific
catalog 800 includes a song and information related to the song.
Additionally, music-specific catalog 800 may include separate entries for
content items related to albums, artists, music videos and other
music-related content items, and the information in various entries may
overlap. For example, a song entry may include a particular CD on which
the song has been included, while an entry for the particular CD may
include the song.

[0109] The music-specific catalog 800 includes a list of song titles 811,
such as, for example, songs having the word "Breathe" in the title, songs
entitled "Big Yellow Taxi" and songs entitled "Holiday." For each song
title, an artist 812 who sings a song having that title is listed, as
well as a first 813 and second 814 (if applicable) album (e.g., CD) on
which the song appears.

[0110] The music-specific catalog 800 also includes a perceived popularity
for each content item (e.g., song) within the music-specific catalog 800.
The perceived popularity is a parameter that may, in some
implementations, include a single number that represents a combination of
a raw popularity score and an ambiguity ratio, as described above. The
entries for some content items within music-specific catalog 800 do not
include a perceived popularity because, for example, no perceived
popularity has been determined for the song or a perceived popularity
determined for the song has become stale, and thus, was removed. Content
items without a perceived popularity may be selected by search system
130, as described above, for determination of a perceived popularity
associated therewith. For example, in an exemplary entry 820, the artist
Blu Cantrell sings a song titled "Breathe," which appears on the album
"Bittersweet" and has a perceived popularity of "40."

[0111] As described above, the music-specific catalog may be used to
determine popularity of a particular song, album, artist or other
music-related digital instance. When providing search results for a
search query associated with a content item, the search system 130 may
visually present search results that are associated with popular songs,
albums or artists in a manner that separates search results that are most
likely to be responsive to the search query from other run of the mill
search results. Thus, the provided search results may more accurately, or
easily, satisfy an information need of the user who entered the search
query.

[0112] GUI 900 of FIG. 9 is structured and arranged to provide search
results for a search query based on popularity of songs associated with
the search results. Perceived popularity for songs associated with the
search results may be determined using, for example, process 500 of FIG.
5, and may be accessed from a genre-specific catalog, such as, for
example, music-specific catalog 800 of FIG. 8. More particularly, GUI 900
provides search results for songs that include the phrase "Big Yellow
Taxi" as a lyric, a title, an album name, or an artist name.

[0113] A search result 910 associated with the Counting Crows version of
the song "Big Yellow Taxi" is provided at the top of the search result
list because the popularity of the Counting Crows version of the song
(e.g., 150 as shown) may be greater than that of other artists (e.g., Amy
Grant, Joni Mitchell and Pinhead Gunpowder) who have performed and/or
recorded the tune, as determined based on a perceived popularity for
content items associated with each of the search results accessed within
a music-specific catalog.

[0114] Although search results associated with Counting Crows may be
provided at the top of the search result list (i.e., the Counting Crows
search results may be "boosted" to the top of the list), search results
associated with other songs having the phrase "Big Yellow Taxi" as a
title also may be provided as search results, but at a lower ranked
position. For example, a search result 920 associated with to the Joni
Mitchell version of the song is the second search result listed, and
thus, may be associated with a perceived popularity (e.g., 100 as shown)
that is close to the perceived popularity for the Counting Crows tune
(e.g., 150 as shown in FIG. 8). As such, the search system 130 may have
determined that although the Counting Crows tune is more popular than the
Joni Mitchell version, the popularity ranking among the two is very
close. Furthermore, a web page for a band called "Big Yellow Taxi" also
appears as a search result 830, though much lower in the search result
list, indicating a lesser popularity (e.g., 15 as shown) and lesser
likelihood that the search result 930 is the most responsive search
result for the search query.

[0115] As described above, a particular search result may be associated
with more than one content item. For example, a search result that refers
to a "Madonna vs. Prince" web page may be determined to be associated
with both the artist Madonna and the artist Prince. Because each of the
artists may be associated with its own perceived popularity, this
particular search result may be deemed to be associated with two
perceived popularity. To determine where to present such a search result
in a list of search results, a single perceived popularity may be
determined for the search result. In a first implementation, the search
system 130 may combine the perceived popularity for the more than one
content item to which the search result refers by, for example, averaging
the two perceived popularity or performing some other mathematical
operation on the two perceived popularity (e.g., addition, subtraction,
multiplication, or division). In a second implementation, the search
system 130 may determine a content item that is more closely related to a
search query that resulted in presentation of the search result and use
the perceived popularity associated with the determined content item as
the perceived popularity for the search result. In a third
implementation, the search system 130 may assign the perceived popularity
associated with one of the content items as the perceived popularity for
the search result, such as, for example, the search system 130 may
determine that a search result is only as popular as its least popular
content item, and, as such, use the lowest perceived popularity as the
perceived popularity for the search result. In a fourth implementation,
the search system 130 may present the search result more than once in a
search result list based on each of the perceived popularity associated
with the more than one content items referred to by the search result.
The first, second, third, and fourth implementations, or a sub-set
thereof, may be used by the search system 130 in combination or
independently.

[0116] GUIs 1000-1200 of FIGS. 10-12, respectively, are structured and
arranged to provide video search results based on popularity of songs
associated with the video search results. Perceived popularity for the
songs may be determined using, for example, process 500 of FIG. 5, and
may be accessed from a genre-specific catalog, such as, for example,
music-specific catalog 800 of FIG. 8.

[0117] More particularly, GUI 1000 provides video search results for the
search query "Big Yellow Taxi." The search system 130 may determine that
a user desires video search results because the user entered the search
query "Big Yellow Taxi" into a specialty video search engine or via some
other indication.

[0118] Search results 1020-1040 are associated with the song "Big Yellow
Taxi" as recorded by the artist Counting Crows. The Counting Crows search
results 1020-1040 may be provided at the top of the search result list
because the popularity of the Counting Crows version of the song may be
greater than that of the other artists (e.g., Amy Grant, Joni Mitchell
and Pinhead Gunpowder) who have performed the tune. Although search
results associated with Counting Crows may be boosted to the top of the
search result list, search results associated with other songs having
"Big Yellow Taxi" as a title or lyric also may be provided as search
results, but at a lower ranked position (not shown). Additionally, or
alternatively, video search results 1020-1040 associated with the
Counting Crows may be boosted to the top of the search result list
because of a popularity for the music video (i.e., a content item that is
different from the song content item) for the Counting Crows version of
the song, and may not necessarily reflect an overall popularity of the
Counting Crows version of the song.

[0119] GUI 1100 provides video search results for the search query
"Breathe." As shown, video search result 1110 is associated with a song
entitled "Breathe" by the artist Fabolous, video search result 1120 is
associated with the artist Faith Hill, video search result 1130 is
associated with the artist Greenwheel and video search 1140 is associated
with the artist Melissa Etheridge. The order in which the video search
results 1110-1140 are provided in GUI 1100 indicates a degree of
popularity of the videos referred to by each search result. For example,
video search result 1110 is provided at the top of the search results
list, and thus, may have been determined to be more popular (and more
likely to be responsive to the search query) than video search result
1140, which is provided at the bottom of the list. More particularly, and
as shown in FIG. 8, the artist Fabolous' version of a song entitled
"Breathe" is associated with a perceived popularity of 125, while the
Faith Hill version is associated with a perceived popularity of 110 and
the Melissa Etheridge version is associated with a perceived popularity
of 25.

[0120] GUI 1200 provides video search results for the search query
"Holiday." As shown, video search result 1210 is associated with the song
"Holiday" by the artist Green Day and video search result 1230 is
associated with a song of the same name by the artist Madonna. A video
search result 1220 associated with the artist Billie Holiday also appears
in the search result list because the artist name satisfies the search
query "Holiday." Again, the placement of the search results 1210-1230 in
the search result list may be based on the popularity of the
corresponding music-related item (e.g., particular song, music video for
the song or artist) that has been determined to satisfy the search query.
For example, the perceived popularity for the Green Day song "Holiday" is
105 as shown in FIG. 8, and the perceived popularity for the Madonna
version is 75, as also shown in FIG. 8. As such, the perceived popularity
associated with the singer Billy Holiday is between 75 and 105 because
search result 1220 is provided in between search results 1210 and 1230.

[0121] In addition to a perceived popularity for a content item, other
criteria also may be used to rank search results presented in response to
a search query provided by a user. In some implementations, information
from a user's interest profile also may be used to determine presentation
of search results. For example, a user input the search query "Madonna."
Based on a perceived popularity, search results that refer to digital
instances that describe the singer Madonna may be presented more
prominently than search results that refer to digital instances that
involve religious connotations for the word "Madonna." However, a user's
interest profile may indicate a strong interest in religious iconography.
As such, the user's interest profile may be used in combination with, or
instead of, perceived popularity in ranking search results for this
particular user. When user interest profile information is used in
combination with perceived popularity information, and other possible
criteria, each of the criteria may be individually weighted to cause one
or more of the criteria to have cause a greater or lesser effect on the
presentation of the search results. More broadly applied, the concepts
described herein may be implemented outside the scope of Internet content
searches exclusively yielding web pages. For instances the concepts may
be applied to enable determination of popularity for information revealed
by the results of searches against file archives or database records
(e.g., which also may be referred to as digital instances), where the
results are ranked based on a determination of perceived popularity of
content items described by, or embodied within, in those digital
instances. In one implementation, the perceived popularity for a content
item may be contrasted with a with a real popularity (e.g., frequency or
absolute number of accesses) of the digital instance itself.

[0122] The described systems, methods, and techniques may be implemented
in digital electronic circuitry, computer hardware, firmware, software,
or in combinations of these elements. Apparatus embodying these
techniques may include appropriate input and output devices, a computer
processor, and a computer program product tangibly embodied in a
machine-readable storage device for execution by a programmable
processor. A process embodying these techniques may be performed by a
programmable processor executing a program of instructions to perform
desired functions by operating on input data and generating appropriate
output. The techniques may be implemented in one or more computer
programs that are executable on a programmable system including at least
one programmable processor coupled to receive data and instructions from,
and to transmit data and instructions to, a data storage system, at least
one input device, and at least one output device. Each computer program
may be implemented in a high-level procedural or object-oriented
programming language, or in assembly or machine language if desired; and
in any case, the language may be a compiled or interpreted language.
Suitable processors include, by way of example, both general and special
purpose microprocessors. Generally, a processor will receive instructions
and data from a read-only memory and/or a random access memory. Storage
devices suitable for tangibly embodying computer program instructions and
data include all forms of non-volatile memory, including by way of
example semiconductor memory devices, such as Erasable Programmable
Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only
Memory (EEPROM), and flash memory devices; magnetic disks such as
internal hard disks and removable disks; magneto-optical disks; and
Compact Disc Read-Only Memory (CD-ROM). Any of the foregoing may be
supplemented by, or incorporated in, specially-designed ASICs
(application-specific integrated circuits).